Biology subjects

Lexa, M.

Publications and source records attributed to Lexa, M..

4 recordsLinked to original sources

bioRxiv2026

Climate-driven in-situ trait variation in an annual ruderal grass across Europe

O_LIPlant functional traits link environmental conditions to plant performance and adaptation. Growing evidence shows that in-situ intraspecific trait variation can be as important as differences between species, yet large intraspecific datasets measured in situ are rare. While most studies focused on plant morphological traits, contents of elemental nutrients in the seeds received much less attention so far. C_LIO_LIWe conducted a large-scale in-situ study of the widespread ruderal grass Hordeum murinum. We sampled 2070 individuals from 207 populations from a large part of the native range in Europe and Northern Africa. In-situ, we measured seed ripening phenology and morphological traits, and analyzed the content of elemental nutrients in the seeds. C_LIO_LIWe found that Hordeum murinum grew larger, produced seeds later, and had heavier seeds in colder and wetter areas. Plants in denser vegetation were taller with heavier seeds but formed fewer spikes. Seed nutrient content generally declined with seed weight and was mainly driven by climate. Soil conditions had only minor effects on plant traits and seed nutrients. Population identity explained much of the variation, indicating a possible genetic component. C_LIO_LISynthesis: Our findings provide a comprehensive view of Hordeum murinum responding to environmental gradients across its European distribution. Climatic variables, especially temperature, are key drivers for reproductive success and seed nutrient content, while local environments, such as biotic pressures, are more critical for growth-related traits. These patterns indicate that H. murinum modulates its growth and reproductive investment along environmental gradients, balancing phenology, stress tolerance, and limited competitive capacity. C_LI

ecology↗

bioRxiv2024

Y chromosome-coded HSATII repeats may contribute to higher incidence of cancer in men

It has been observed that men have a shorter lifespan and higher incidence of certain cancers than women. We postulate here that this phenomenon may be attributed to the "toxic Y chromosome", owing to the expression of normally silent HSATII repeats in cancer, abundantly present on chromosome Y. Investigating the contribution of all repetitive elements to chromatin interactions using a genome-wide HiC set derived from a colon cancer cell line (HCT116), we found several anomalies regarding satellite HSATII elements when compared to other genomic repeat types: (i) HSATII repeats tend to form HiC pairs mostly with their own kind, and depleted in most heterologous repeat pairs, whereas other types of repeats readily form heterologous HiC pairs; (ii) when treated with 5aza, a chemotherapeutic agent, the number of long-distance HSATII-overlapping HiC pairs significantly decreased when compared to the untreated samples; (iii) and most importantly, in the treated samples interchromosomal HiC read pairs involving a chromosome Y-located repeat and an HSATII repeat on either end almost completely disappear. (iv) We also found that HSATII repeats lack overlap with other types of repeats. To account for these anomalies we queried non-B DNA structure forming such as G-quadruplexes and transcription-related triplex forming. We found that Y chromosome-coded HSATII repeats form significantly stronger triplexes than HSATII repeats on other chromosomes, their strength significantly correlating with the number of HiC reads they overlap with. We surmise that the above phenomena may cause the higher incidence of cancer in men and may lead to the disappearance of the Y chromosome observed in cancerous cells in men.

bioinformatics↗

bioRxiv2024

Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.

BackgroundLong terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key compounds, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs. ResultsWe used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting - GBC), (ii) hybrid CNN-LSTM models, and (iii) a pre-trained transformer-based model (DNABERT) using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the CNN-LSTM hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. SHAP positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box, and TG..CA patterns around both LTR edges. ConclusionsOur results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5- and 3-edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.

bioinformatics↗

bioRxiv2021

HiC-TE: a computational pipeline for Hi-C data analysis shows a possible role of repeat family interactions in the genome 3D organization

The role of repetitive DNA in the 3D organization of the interphase nucleus in plant cells is a subject of intensive study. High-throughput chromosome conformation capture (Hi-C) is a sequencing-based method detecting the proximity of DNA segments in nuclei. We combined Hi-C data, plant reference genome data and tools for the characterization of genomic repeats to build a Nextflow pipeline identifying and quantifying the contacts of specific repeats revealing the preferential homotypic interactions of ribosomal DNA, DNA transposons and some LTR retrotransposon families. We provide a novel way to analyze the organization of repetitive elements in the 3D nucleus.

bioinformatics↗